Method and System for Generating a Representation of a Finger Print Minutiae Information

ABSTRACT

The invention relates to a method for generating a representation of a finger print minutiae information. The invention also relates to a method for generating a representation of a finger print for biometric template protection purposes Biometric template protection techniques provide technological means to protect the privacy of biometric reference information stored in biometric. systems These methods stand in sharp contrast to approaches where biometric information is protected only by legislation and procedures around storage facilities. These systems are not reliable as they are susceptible to human and procedural errors. Template protection guarantees the protection of biometric information without the assumption that individuals are trusted or procedures are properly implemented.

The invention relates to a method for generating a representation of afinger print minutiae information.

The invention also relates to a method for generating a representationof a finger print for biometric template protection purposes.

Biometric template protection techniques provide technological means toprotect the privacy of biometric reference information stored inbiometric systems. These methods stand in sharp contrast to approacheswhere biometric information is protected only by legislation andprocedures around storage facilities. These systems are not reliable asthey are susceptible to human and procedural errors. Template protectionguarantees the protection of biometric information without theassumption that individuals are trusted or procedures are properlyimplemented.

Template protection techniques transform classical representations ofbiometric references (e.g. the image of an iris, a feature vectorderived from a face, etc.) into a so-called secure template. Thesesecure templates are constructed such that it is very hard to retrieveinformation regarding the original biometric sample. Furthermore,matching is done directly on the secure template. Lastly, in manyimplementations of template protection it is possible to derive severaldistinct secure templates from a single biometric characteristic(renewability).

Template protection brings huge benefits for biometric systems.Centralised database can be constructed in compliance with privacy laws.Distinct templates from the same biometric characteristic can also begenerated for different applications, in order to reduce/eliminatepossibility of cross matching. Revocation and reissue is feasible in thecase of such template compromise. Risks of spoofing attack using storedor transmitted template can also be prevented.

Template protection is based on the application of cryptographic hashfunctions that are applied on a binary string representation of thebiometric reference information. The three basic properties of acryptographic hash function are that it is a one-way function (pre-imageresistance), that it is difficult to find a second hash input thatyields the same value as for a given hash input (second pre-imageresistance), and that it is difficult to find two inputs that yield thesame value (collision resistance).

A powerful method for privacy protection is a so-called helper-dataapproach. In the helper data approach, the result of biometric templateprotection is a unique string (the so-called Pseudo Identity) and thepublic helper data (Diversification Code). It is of crucial importancein this context that this unique string is protected since knowledge ofthat secret would allow an attacker to reveal a substantial amount ofbiometric information from the public helper data. The general objectiveis thus to generate a bit string that is irreversibly derived from abiometric template.

To allow more flexibility a second level of bit strings, orpseudo-identities, may be introduced. These are derived from the firstbit string and not directly from the biometric template. The basicrequirements may be repeated, i.e., absolute irreversibility andunlinkability.

The use of fingerprints for verification is especially interesting giventhe good verification performance and the low-cost sensors. Basicallytwo approaches can be pursued when using fingerprints: shape-basedmatching and minutiae matching.

For shape-based matching, the pattern of ridges is used as a 2D imageand compared against another 2D image. In most cases, dedicatedalignment methods (to resolve possible translation and rotations thatmay occur during various measurements) are required to obtain goodperformance.

For minutiae-based matching, the minutiae locations, possiblyaccompanied by other information such as a minutiae type (ridge endingor bifurcation), minutiae orientation, quality, are used to comparefingerprints.

Such comparison technique is for example described in “Fast FingerprintVerification Using Subregions of Fingerprint Images” by K. C. Chan, Y.S. Moon and P. S. Cheng (IEEE Transactions on Circuits and Systems forVideo Technology, Vol. 14, No. 1, January 2004). During enrollment afingerprint image is captured and minutiae are extracted and stored. Forverification purposes a fingerprint image is captured again and theextracted minutiae information is compared with the stored minutiaeinformation. In order to improve the processing speed a subregion of thefingerprint is used for capturing and authentication purposes.

When using template protection, however, the minutiae-based matching hastwo major difficulties.

First, a list of minutiae locations and their attributes do not form asampled function in a well defined coordinate system. For templateprotection schemes, however, the feature data should be represented as asampled function in a well defined coordinate. This hence requires aconversion from minutiae location to a fixed-length, sample domainfeature vector.

Furthermore, since template protection prohibits access to the originaldata (such as minutiae locations), it is impossible to align enrolmentand verification data. Hence any processing to resolve translations,rotations, scaling and/or non-linear transformations should either bemitigated during the transformation process or performed aspre-processing step during template generation, rather than an alignmentstep during matching.

Even without template protection, the conversion of unordered,variable-length data into a fixed-length, sample domain feature set hasbenefits in terms of matching efficiency since relatively simple andfast matching algorithms can be employed.

It is noted that US20070266427A1 discloses a method to generate afixed-length, sample domain feature set from a set of minutiae locationsin a finger print. This method creates a 2D pattern of minutiae densityfunctions that is subsequently processed in the frequency domain toresult in a translation-invariant representation. However the methodaccording to US20070266427A1 have the following shortcomings. First isrequires a very large data size, but it is also very sensitive torotational distortion. Moreover it involves a number of computationallycomplex operations.

In this application we mitigate at least one of the shortcomings of themethod specified in US20070266427A1.

According to the invention the method is characterized by the steps of:

-   -   obtaining minutiae information such as locations, orientation,        type and quality;    -   obtaining at least one reference point P in said finger print;    -   determining sampled minutiae function values using said minutiae        information and said reference point;    -   generating a vector representation based on said sampled        minutiae function values.

Likewise the system according to the invention is characterized in thatit comprises

-   -   capturing means for capturing the finger print of a person;    -   image processing means arranged for processing said finger print        being captured in a finger print image; as well as    -   determining said minutiae locations in said finger print image;    -   determining at least one reference point P in said finger print;    -   determining ordered minutiae values as functions of said        minutiae parameters and relative minutiae locations with respect        to said reference point and    -   generating a vector representation based on said distinct        distance values.

The use of such a reference point considerably improves performance of aminutiae transformation function and also reduces the complexity of thefeature representation and its derivation. Furthermore by incorporatingthe orientation of the minutiae locations with repsect to the referencepoint improves the sensitivity to rotational errors. Moreover, as theresulting feature is very compact, it enables an efficient method foruse in biometric encryption and protection.

More particularly the method and system are characterized characterizedby the features of:

-   -   defining an imaginary circle C_(R) around said reference point        P;    -   projecting each minutiae location on said imaginary circle;    -   determining the distinct projected value of each minutiae        location as a feature vector;    -   generating said vector representation based on said feature        vectors.

The essential feature of the invention is to employ a circularrepresentation of minutiae density functions using a stable referencepoint. Such a reference point could be the location of a core, a delta,a weighted average of minutiae coordinates, or alike. The use of such areference point results in (1) a translation-invariant presentation, and(2) a relatively simple (1D but preferably complex-valued)representation of minutiae and (3) a representation that allows the useof minutia orientation information as integral part of the presentation,and (4) enables the formation of a reference coordinate system for asample domain representation.

These and other aspects of the invention will be further elucidated anddescribed with reference to the drawings.

Throughout the drawings, the same reference numeral refers to the sameelement, or an element that performs the same function.

In the FIG. 1, for minutia index i (i=1, . . . , l), a set of minutiaelocations is given by the coordinates x_(i), y_(i) and a minutiaorientation angle is given by α_(i), (see FIG. 1). A reference point isdefined by the coordinate x_(R), y_(R). This reference point sets theorigin of a coordinate system from which the set of minutiae locationswith coordinates x_(i), y_(i) are defined. For n=1, 2 . . . N, a set ofcircular spatial filters F_(n)(β) are defined that form (preferablypartially overlapping) analysis windows on the circle C_(R) centered atthe reference point x_(R), y_(R), where β is an angle argument, with−π≦β≧π. Examples of the spatial filters drawn on the circle C_(R) areshown in FIG. 2. Visualization in one dimension is shown in FIG. 3.

In one preferred embodiment the output of each spatial filter G_(n) isobtained by summing the contribution of each minutia point taking intoaccount the following three parameters:

-   -   1. the distance of each minutia with respect to the reference        point using a distance variation function w(d_(i)),    -   2. the spatial filter weight F_(n)(β) and    -   3. the orientation α_(i) of the minutia

More specifically, in the preferred embodiment, G_(n) is given by:

$\left. {G_{n} = {\sum\limits_{i}{{F_{l}\left( \beta_{i} \right)}{w\left( d_{i} \right)}\exp}}} \right){j\left( {\alpha_{i} - \beta_{i}} \right)}$

where d_(i) is the distance between the location of minutia i and thereference point x_(R), y_(R):

d _(i)=√{square root over ((x _(i) −x _(R))²+(y _(i) −y _(R))²)}{squareroot over ((x _(i) −x _(R))²+(y _(i) −y _(R))²)}

β_(i) is the angle of the minutia location with respect to the referencepoint x_(R), y_(R):

$\beta_{i} = {\arctan \left( \frac{y_{i} - y_{R}}{x_{i} - x_{R}} \right)}$

and the distance variation function w(d_(i)) is preferably amonotonically increasing or decreasing function with distance, and iszero for large distances to exclude outlier minutiae locations.

G_(n) has the following unique properties that make it suited forefficient biometric classification and template protection applications.

-   -   1. It exists in a common sample domain, enabling simple        sample-wise vector comparison between different measurements.    -   2. It has a graceful property in that missing minutiae points do        not affect its values significantly.    -   3. Minutiae attributes such as minutiae direction, type and        quality are embedded into the function values.

Various features can be deduced from the values G_(n). For example, onecould use the absolute value of G_(n) as an orientation-invariantminutiae distance density, the real part of G_(n) as a distance densityfor minutiae that have an orientation perpendicular to the projectionaxis, and the imaginary part of G_(n) as the distance density ofminutiae with an orientation that is parallel to the projection axis.

The features can then be collected into a vector to form featurevectors. For instance a feature vector FV(|G|) can be formed as

FV(|G|)=[|G ₁ ||G ₂ | . . . |G _(N)|]

Similarly, features derived from G_(n) such as the orientation ormagnitude density etc, or G_(n) itself can be used to construct featurevectors.

Further extension of a feature vector may comprise the variance ofminutiae orientations within the spatial segments. Let N_(n) be thenumber of minutia in the n-th spatial segment, then these can beobtained as follows:

with S_(n,RE) the variance in the direction perpendicular to theprojection axis, and S_(n,IM) the variance parallel to the projectionaxis.

Finally, additional fingerprint attributes such as the total number ofminutiae, the orientation of the reference point, the number of deltasand cores can be used as additional feature data.

In some situations, it is preferable to combine minutiae representationsobtained using several different stable reference points into a singlerepresentation. For instance, a set of representations obtained usingdifferent minutiae locations as reference points can be combined to forma single minutiae representation. To be more specific, for k=1 . . . K,let S_(n,k) represent a feature obtained by taking the k-th minutiae asthe reference stable point. Then, in one preferred embodiment a featureS_(n) is obtained as a linear combination of S_(n,k), i.e.,

$S_{n,{RE}} = {{\frac{1}{N_{n}}{\sum\limits_{i}{{F_{n}\left( \beta_{i} \right)}\left( {{w\left( d_{i} \right)}{\cos \left( {\alpha_{i} - \beta_{i}} \right)}} \right)^{2}}}} - \left( {\sum\limits_{i}{{F_{n}\left( \beta_{i} \right)}\left( {{w\left( d_{i} \right)}{\cos \left( {\alpha_{i} - \beta_{i}} \right)}} \right)}} \right)^{2}}$$S_{n,{IM}} = {{\frac{1}{N_{n}}{\sum\limits_{i}{{F_{n}\left( \beta_{i} \right)}\left( {{w\left( d_{i} \right)}{\sin \left( {\alpha_{i} - \beta_{i}} \right)}} \right)^{2}}}} - \left( {\sum\limits_{i}{{F_{n}\left( \beta_{i} \right)}\left( {{w\left( d_{i} \right)}{\sin \left( {\alpha_{i} - \beta_{i}} \right)}} \right)}} \right)^{2}}$$\mspace{20mu} {S_{n} = {\frac{1}{N}{\sum\limits_{k = 1}^{K}{\eta_{k}S_{n,k}}}}}$

where η_(k) is a weight function that is chosen by, for example, takinginto account reliability of the reference point.

In one preferred embodiment, the minutiae are grouped into clustersaccording to their radial distance from the reference stable point (orany other clustering method). In this case, each cluster isindependently used to generate a feature vector. Subsequently, theindividual feature vectors generated using the different clusters arecombined to form a single minutiae representation feature vector.

A device incorporating the aspects of the invention is disclosed in FIG.4 and is denoted with reference numeral 100. The device 100 can be ahand held device or an access control device. The invention can beincorporated in for example a biometric voting device and comprisescapturing means 110 for capturing the finger print 210 of a person'sfinger 200.

The processing means 120 are arranged for generating a template based onthe captured finger print 210 according to the principles of theinvention. According to the invention said processing means 120 canlocate or determine one or more minutiae locations in said finger print210 being captured and to define at least one reference point Pin saidfinger print 210 as depicted in FIGS. 1 and 2.

Next for each determined minutiae location ordered minutiae values arecalculated as a function of minutiae parameters and relative minutiaelocations with respect to the reference point P.

Subsequent the device generates a vector representation based on saiddistinct distance values.

1. Method for generating a representation of a finger print minutiaeinformation comprising the steps of: obtaining said minutiaeinformation; obtaining at least one reference point P in said fingerprint; determining a coordinate system based on the said reference pointP; determining sampled functions values of said minutiae information inthe said coordinate system; generating a vector representation based onsaid sampled function values.
 2. Method according claim 1, where saidcoordinate system is characterized by the step of: defining an imaginarycircle C_(R) around said reference point P.
 3. Method according claim 1,wherein said sampled function values are characterized by the step of:determining distinct feature sample values by projecting said minutiaeinformation on to the said imaginary circle C_(R); generating a featurevector representation based on the said distinct feature sample values.4. Method according to claim 1, characterized by the step of using aplurality of said reference points P to generate a plurality of featurevectors.
 5. Method according to claim 4, characterized by the step ofcombining said plurality of feature vectors into a single featurevector.
 6. Method according to claim 5, characterized in that saidcombination of said plurality of feature vectors is a linear combinationof the individual vectors.
 7. Method according to claim 1, characterizedin that the said reference point P is at least one or a group of singleor multiple core points of the finger print; single or multiple deltapoints of the finger print; single or multiple minutiae locations; amass center of a finger print image; a mass center of minutiaelocations.
 8. Method according to claim 1, wherein said minutiaeinformation comprises at least one or more of a minutiae direction; aminutiae type; a minutiae quality; location information of said minutiaewith respect to said reference point P.
 9. A method as in claim 1wherein the minutia are grouped into a plurality of distinct sets, andthe contribution of the said plurality of distinct sets are merged intoindependent feature vectors or combinations thereof.
 10. A method as inclaim 9 wherein the said clustering is achieved according to theirradial distance from the reference stable point.
 11. System forgenerating a representation of a finger print minutiae locationscomprising: capturing means for capturing the finger print of a person;image processing means arranged for processing said finger print beingcaptured in a finger print image; as well as determining said minutiaelocations in said finger print image; determining at least one referencepoint P in said finger print; determining ordered minutiae values asfunctions of said minutiae parameters and relative minutiae locationswith respect to said reference point; and generating a vectorrepresentation based on said distinct distance values.